發(fā)布時間：2018-04-03 22:47

  本文選題：蜂群算法　切入點(diǎn)：擁擠度　出處：《曲阜師范大學(xué)》2017年碩士論文

【摘要】：人工蜂群算法是一種穩(wěn)定、高效的群體智能優(yōu)化算法,它受到蜜蜂集體覓食行為的啟發(fā),在解決大多數(shù)問題時均表現(xiàn)出良好的性能。相較其它優(yōu)化算法,它在尋優(yōu)等方面有著收斂速度快、魯棒性好、全局收斂、適用范圍寬等優(yōu)勢,適用于多種類的優(yōu)化,對有約束和無約束條件下的優(yōu)化問題均有很強(qiáng)的應(yīng)用價值。本論文對基本人工蜂群算法的相關(guān)知識進(jìn)行了闡述。該算法需要設(shè)置的參數(shù)少、魯棒性強(qiáng)、復(fù)雜度低,而且算法執(zhí)行時能兼顧全局搜索和局部搜索,增加了獲取最優(yōu)極值的概率。本章也討論了該算法存在的弊端,如:收斂易早熟、搜索精度較低、難以跳出局部極值等。本文首次引入“擁擠度”這一概念來改進(jìn)基本人工蜂群算法。我們將通過定義一個擁擠度公式來調(diào)配人工蜂,限制采蜜峰的數(shù)量,并借此參數(shù)合理調(diào)控鄰域搜索。當(dāng)擁擠度低值時蜂群不需要進(jìn)行任何調(diào)整,而當(dāng)擁擠度高時將調(diào)用改進(jìn)的觀察蜂跟隨公式,適當(dāng)減少本區(qū)域采蜜蜂的數(shù)量;隨后會增加偵查蜂的數(shù)量以擴(kuò)大對解空間的全局搜索,這樣就能在某種程度上幫助算法避免早熟現(xiàn)象,同時在后期提高算法的收斂速度。本文另一個改進(jìn)思路是設(shè)定蜂群中偵查蜂始終存在,一般使其比例保持在蜂群總數(shù)的5%-10%左右,以此維持蜂群的多樣性,以便繼續(xù)保持對解空間的不斷搜索。此方案的實(shí)施有助于人工蜂全局檢索能力的提升,進(jìn)一步加速算法執(zhí)行后期的收斂。文章中討論了網(wǎng)絡(luò)服務(wù)質(zhì)量(QoS)的由來,以及QoS度量、QoS服務(wù)體系模型、QoS路由分類等。QoS路由即端到端傳輸時選擇傳輸鏈路,此鏈路要求符合QoS度量中的各條件限制。這些概念和相關(guān)的研究背景將為下一章打下基礎(chǔ)。本文把改進(jìn)的人工蜂群算法用于解決實(shí)際網(wǎng)絡(luò)的QoS路由問題。該算法將通過人工蜂檢索全部符合丟包率、帶寬、延遲抖動、時延、等限定情況下的可行鏈路,以此確定組播路由的最優(yōu)鏈路。我們通過仿真實(shí)驗觀察兩種算法在實(shí)際應(yīng)用中的表現(xiàn),先通過Dijkstra前N條路徑算法構(gòu)建非劣解集,人工蜂在非劣解集中執(zhí)行鄰域搜索行為以獲取適應(yīng)度更高的優(yōu)質(zhì)解。最后對比這兩種算法求解最優(yōu)鏈路時所花費(fèi)的代價、平均迭代次數(shù)等指標(biāo),以此證明引入擁擠度參數(shù)后的改進(jìn)算法實(shí)用性良好。
[Abstract]:Artificial bee colony algorithm is a stable and efficient swarm intelligence optimization algorithm. It is inspired by the collective foraging behavior of bees and shows good performance in solving most of the problems.Compared with other optimization algorithms, it has the advantages of fast convergence speed, good robustness, global convergence, wide application range and so on. It is suitable for many kinds of optimization.Both constrained and unconstrained optimization problems have strong application value.In this paper, the basic knowledge of artificial bee colony algorithm is described.The algorithm needs few parameters, strong robustness, low complexity, and the algorithm can take into account the global search and local search, thus increasing the probability of obtaining the optimal extremum.In this chapter, we also discuss the disadvantages of this algorithm, such as premature convergence, low searching precision and difficulty to jump out of the local extremum and so on.In this paper, the concept of "crowding degree" is introduced for the first time to improve the basic artificial bee colony algorithm.We will define a crowding formula to allocate artificial bees to limit the number of honey peaks collected and use this parameter to regulate the neighborhood search reasonably.When the crowding degree is low, the colony does not need any adjustment, but when the crowded degree is high, the improved observation bee follow formula will be called to reduce the number of bees collected in this area.Subsequently, the number of detection bees will be increased to expand the global search of the solution space, which can help the algorithm to avoid precocity to some extent and improve the convergence speed of the algorithm in the later stage.In this paper, another way of improving is to set the detection bee in the colony to always exist, and generally keep its proportion at about 5- 10% of the total number of bees, so as to maintain the diversity of the colony, so as to keep the continuous search of the solution space.The implementation of this scheme is helpful to enhance the global retrieval ability of human worker bees and further accelerate the convergence of the algorithm in the later stage of execution.This paper discusses the origin of QoS (quality of Service) in network, and the choice of transmission link when end-to-end transmission, such as QoS metrics, QoS routing classification and so on. This link needs to meet the constraints of QoS metrics.In this paper, the improved artificial bee colony algorithm is used to solve the QoS routing problem in real networks.In order to determine the optimal link of multicast routing, the algorithm will retrieve all feasible links under limited conditions such as packet loss rate, bandwidth, delay jitter, delay, etc.We observe the performance of the two algorithms in practical application through simulation experiments. First, we construct the non-inferior solution set by the N-path algorithm before Dijkstra, and the worker bee performs neighborhood search behavior in the non-inferior solution set to obtain a higher fitness solution.Finally, the cost and the average number of iterations of the two algorithms are compared to prove the practicability of the improved algorithm.
【學(xué)位授予單位】：曲阜師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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